Load Testing
Load testing answers critical questions: How many users can your system handle? What happens when traffic spikes? Where does performance degrade first? Finding these answers in testing beats discovering them during a production incident.
Types of Load Tests
Load tests simulate expected traffic levels. They verify your system handles normal operations with acceptable performance. Run these regularly to catch regressions.
Stress tests push beyond expected limits to find breaking points. How many concurrent users until response times become unacceptable? When do errors start appearing? Knowing your limits helps you plan capacity.
Spike tests simulate sudden traffic increases — a viral post, a marketing campaign launch, or a flash sale. Can your system handle 10x normal traffic appearing in seconds?
Soak tests maintain steady load over extended periods — hours or days. They reveal memory leaks, connection pool exhaustion, and other problems that only appear over time.
Load Testing Tools
k6 offers a developer-friendly approach with JavaScript test scripts:
import http from 'k6/http';
import { check, sleep } from 'k6';
export const options = {
vus: 100,
duration: '5m',
};
export default function() {
const res = http.get('https://api.example.com/products');
check(res, {
'status is 200': (r) => r.status === 200,
'response time < 500ms': (r) => r.timings.duration < 500,
});
sleep(1);
}
This simulates 100 virtual users making requests for 5 minutes, checking that responses are successful and fast.
Locust uses Python and supports distributed testing across multiple machines. Artillery provides YAML configuration for simpler scenarios. Apache JMeter offers a GUI and extensive protocol support.
Interpreting Results
Watch for these patterns in your results:
Latency degradation shows when response times increase as load increases. Gradual increases suggest resource constraints; sudden jumps indicate bottlenecks.
Error rate changes reveal capacity limits. A system handling 1000 requests per second with 0% errors might show 5% errors at 1500 RPS — that's your practical limit.
Resource saturation in CPU, memory, or database connections often explains performance degradation. Correlate load test results with system metrics.
Testing Best Practices
Test against production-like environments. Testing against a single server when production uses ten gives misleading results.
Use realistic data and scenarios. If real users browse products before purchasing, your tests should too.
Start small and increase gradually. Ramping up load helps you identify exactly when problems begin.
Document your baselines. Without knowing that your API handled 500 RPS last month, you can't tell if today's 400 RPS represents a regression.